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Data-Centric Machine Learning with Python

Data-Centric Machine Learning with Python

By : Jonas Christensen, Nakul Bajaj, Manmohan Gosada
4.6 (5)
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Data-Centric Machine Learning with Python

Data-Centric Machine Learning with Python

4.6 (5)
By: Jonas Christensen, Nakul Bajaj, Manmohan Gosada

Overview of this book

In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.
Table of Contents (17 chapters)
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1
Part 1: What Data-Centric Machine Learning Is and Why We Need It
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4
Part 2: The Building Blocks of Data-Centric ML
7
Part 3: Technical Approaches to Better Data
10
Chapter 7: Using Synthetic Data in Data-Centric Machine Learning
13
Part 4: Getting Started with Data-Centric ML

Part 1: What Data-Centric Machine Learning Is and Why We Need It

In this part, we take a deep dive into data-centric machine learning, contrasting it with model-centric approaches. We use real-life examples to illustrate their differences and explore the evolution of AI and ML toward a data-centric perspective. We also dispel the myth of “big data,” highlighting the importance of quality over quantity, and the potential for democratizing ML solutions. Prepare for a fresh perspective on the transformative power of data in ML.

This part has the following chapters:

  • Chapter 1, Exploring Data-Centric Machine Learning
  • Chapter 2, From Model-Centric to Data-Centric – ML’s Evolution
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